| Title: | PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts |
| Journal: | Genome Medicine |
| Published: | 14 Jul 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40653480/ |
| DOI: | https://doi.org/10.1186/s13073-025-01505-w |
| URL: | https://genomemedicine.biomedcentral.com/counter/pdf/10.1186/s13073-025-01505-w |
Publication 16179
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Abstract
BackgroundThe polygenic score (PGS) is an estimate of an individual's genetic susceptibility to a specific complex trait and has been instrumental to the development of precision medicine. As an increasing number of genome-wide association studies (GWAS) have emerged, numerous sophisticated statistical and computational methods have been developed to facilitate the PGS construction. However, both the complex statistical estimation procedure and the various data formats of summary statistics and reference panel make the PGS calculation challenging and not easily accessible to researchers with limited statistical and computational backgrounds.ResultsHere, we propose PGSFusion, a webserver designed to carry out PGS construction for targeting variety of analytic requirements while requiring minimal prior computational knowledge. Implemented with well-established web development technologies, PGSFusion streamlines the construction of PGS using 17 PGS methods in four categories: 11 single-trait, one multiple-trait, two annotation-based and three cross-ancestry based methods. In addition, PGSFusion also utilizes UK Biobank data to provide two kinds of in-depth analyses for 201 complex traits: i) prediction performance evaluation to display the consistency between PGS and specific traits and the effect size of PGS in different genetic risk groups; ii) joint effect analysis to investigate the interaction between PGS and covariates, as well as the effect size of covariates in different genetic subgroups. PGSFusion benchmarks the prediction performances for different methods in one summary statistics. PGSFusion automatically identifies the required parameters in different data formats of uploaded GWAS summary statistics files, provides a selection of suitable methods, and outputs calculated PGSs and their corresponding epidemiological results. Finally, we showcase three case studies in different application scenarios, highlighting its versatility and values to researchers.ConclusionsOverall, PGSFusion presents an easy-to-use, effective, and extensible platform for PGS construction, promoting the accessibility and utility of PGS for researchers in the field of precision medicine. PGSFusion is freely available at http://www.pgsfusion.net/.</p>
7 Keywords
- Biological Specimen Banks
- Computational Biology
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Multifactorial Inheritance
- Software
7 Authors
- Sheng Yang
- Xiangyu Ye
- Xiaolong Ji
- Zhenghui Li
- Min Tian
- Peng Huang
- Chen Cao
1 Application
| Application ID | Title |
|---|---|
| 144904 | Integrative Deep Learning Analysis of UKBB Data: Bridging Genotypes, Medical Imaging, and Polygenic Scores for Precision Health Insights |
Enabling scientific discoveries that improve human health